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Journal of Intelligent Manufacturing

Erschienen in:

22.11.2021

Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization

verfasst von: Yiying Zhang, Aining Chi

Erschienen in: Journal of Intelligent Manufacturing | Ausgabe 4/2023

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Abstract

Most of the reported metaheuristic methods need the control parameters except the essential population size and terminal condition. When these methods are used for solving an unknown problem, how to set the most suitable values for their control parameters to achieve the optimal solution is a great challenge. Group teaching optimization algorithm (GTOA) is a newly presented metaheuristic method, whose remarkable feature is that it only relies on the essential population size and terminal condition for optimization. However, GTOA may get trapped in the local optimal solutions for solving complex optimization problems due to the lack of communication between outstanding group and average group. In order to improve the performance of GTOA, this paper proposes a new variant of GTOA, namely group teaching optimization algorithm with information sharing (ISGTOA). Like GTOA, ISGTOA doesn’t introduce any other control parameters, which enhances the communication between outstanding group and average group by reusing the individuals in the built two archives. The performance of ISGTOA is investigated by CEC 2014 test suite, CEC 2015 test suite, and four challenging constrained engineering design problems. Experimental results prove the superiority of ISGTOA for expensive optimization problems with multimodal properties by comparing with GTOA and other powerful methods. The source codes of the proposed ISGTOA can be found in https://ww2.mathworks.cn/matlabcentral/fileexchange/98629-the-source-code-of-isgtoa and https://github.com/jsuzyy/The-source-code-of-ISGTOA-for-global-optimization.

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Abualigah, L., Diabat, A., Mirjalili, S., Abd Elaziz, M., & Gandomi, A. H. (2021a). The arithmetic optimization algorithm. Computer Methods in Applied Mechanics and Engineering, 376, 113609. https://doi.org/10.1016/j.cma.2020.113609CrossRef

Abualigah, L., Yousri, D., Abd Elaziz, M., Ewees, A. A., Al-qaness, M. A. A., & Gandomi, A. H. (2021b). Aquila optimizer: A novel meta-heuristic optimization algorithm. Computers & Industrial Engineering, 157, 107250. https://doi.org/10.1016/j.cie.2021.107250CrossRef

Askarzadeh, A. (2016). A novel metaheuristic method for solving constrained engineering optimization problems: Crow search algorithm. Computers & Structures, 169, 1–12. https://doi.org/10.1016/j.compstruc.2016.03.001CrossRef

Azizi, M. (2021). Atomic orbital search: A novel metaheuristic algorithm. Applied Mathematical Modelling, 93, 657–683. https://doi.org/10.1016/j.apm.2020.12.021CrossRef

Bhandari, A. K. (2020). A novel beta differential evolution algorithm-based fast multilevel thresholding for color image segmentation. Neural Computing and Applications, 32(9), 4583–4613. https://doi.org/10.1007/s00521-018-3771-zCrossRef

Brajević, I., & Ignjatović, J. (2019). An upgraded firefly algorithm with feasibility-based rules for constrained engineering optimization problems. Journal of Intelligent Manufacturing, 30(6), 2545–2574. https://doi.org/10.1007/s10845-018-1419-6CrossRef

Chou, J.-S., & Truong, D.-N. (2021). A novel metaheuristic optimizer inspired by behavior of jellyfish in ocean. Applied Mathematics and Computation, 389, 125535. https://doi.org/10.1016/j.amc.2020.125535CrossRef

Coello Coello, C. A. (2000). Use of a self-adaptive penalty approach for engineering optimization problems. Computers in Industry, 41(2), 113–127. https://doi.org/10.1016/S0166-3615(99)00046-9CrossRef

Dhiman, G., & Kumar, V. (2017). Spotted hyena optimizer: A novel bio-inspired based metaheuristic technique for engineering applications. Advances in Engineering Software, 114, 48–70. https://doi.org/10.1016/j.advengsoft.2017.05.014CrossRef

Dhiman, G., & Kumar, V. (2018). Emperor penguin optimizer: A bio-inspired algorithm for engineering problems. Knowledge-Based Systems, 159, 20–50. https://doi.org/10.1016/j.knosys.2018.06.001CrossRef

Dhiman, G., & Kumar, V. (2019). Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems, 165, 169–196. https://doi.org/10.1016/j.knosys.2018.11.024CrossRef

Du, T.-S., Ke, X.-T., Liao, J.-G., & Shen, Y.-J. (2018). DSLC-FOA: Improved fruit fly optimization algorithm for application to structural engineering design optimization problems. Applied Mathematical Modelling, 55, 314–339. https://doi.org/10.1016/j.apm.2017.08.013CrossRef

Eskandar, H., Sadollah, A., Bahreininejad, A., & Hamdi, M. (2012). Water cycle algorithm—A novel metaheuristic optimization method for solving constrained engineering optimization problems. Computers & Structures, 110–111, 151–166. https://doi.org/10.1016/j.compstruc.2012.07.010CrossRef

Gandomi, A. H., Yang, X.-S., & Alavi, A. H. (2011). Mixed variable structural optimization using Firefly algorithm. Computers & Structures, 89(23), 2325–2336. https://doi.org/10.1016/j.compstruc.2011.08.002CrossRef

Goldberg, D. E., & Holland, J. H. (1988). Genetic algorithms and machine learning. Machine Learning, 3(2), 95–99. https://doi.org/10.1023/A:1022602019183CrossRef

Gu, L., Yang, R., Tho, C.-H., Makowski, M., Faruque, O., & Li, Y. (2001). Optimization and robustness for crashworthiness of side impact. International Journal of Vehicle Design, 26(4), 348–360.CrossRef

Gunduz, M., & Aslan, M. (2021). DJAYA: A discrete Jaya algorithm for solving traveling salesman problem. Applied Soft Computing. https://doi.org/10.1016/j.asoc.2021.107275CrossRef

Han, L., Xing, K., Chen, X., & Xiong, F. (2018). A Petri net-based particle swarm optimization approach for scheduling deadlock-prone flexible manufacturing systems. Journal of Intelligent Manufacturing, 29(5), 1083–1096. https://doi.org/10.1007/s10845-015-1161-2CrossRef

Hatamlou, A. (2013). Black hole: A new heuristic optimization approach for data clustering. Including Special Section on New Trends in Ambient Intelligence and Bio-Inspired Systems, 222, 175–184. https://doi.org/10.1016/j.ins.2012.08.023CrossRef

He, Q., & Wang, L. (2007a). An effective co-evolutionary particle swarm optimization for constrained engineering design problems. Engineering Applications of Artificial Intelligence, 20(1), 89–99. https://doi.org/10.1016/j.engappai.2006.03.003CrossRef

He, Q., & Wang, L. (2007b). A hybrid particle swarm optimization with a feasibility-based rule for constrained optimization. Applied Mathematics and Computation, 186(2), 1407–1422. https://doi.org/10.1016/j.amc.2006.07.134CrossRef

Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028CrossRef

Huang, F., Wang, L., & He, Q. (2007). An effective co-evolutionary differential evolution for constrained optimization. Applied Mathematics and Computation, 186(1), 340–356. https://doi.org/10.1016/j.amc.2006.07.105CrossRef

Huang, J., Gao, L., & Li, X. (2015). An effective teaching-learning-based cuckoo search algorithm for parameter optimization problems in structure designing and machining processes. Applied Soft Computing, 36, 349–356. https://doi.org/10.1016/j.asoc.2015.07.031CrossRef

Ibrahim, I. A., Hossain, M. J., Duck, B. C., & Fell, C. J. (2020). An adaptive wind-driven optimization algorithm for extracting the parameters of a single-diode PV cell model. IEEE Transactions on Sustainable Energy, 11(2), 1054–1066. https://doi.org/10.1109/TSTE.2019.2917513CrossRef

Kaveh, A., & Dadras, A. (2017). A novel meta-heuristic optimization algorithm: Thermal exchange optimization. Advances in Engineering Software, 110, 69–84. https://doi.org/10.1016/j.advengsoft.2017.03.014CrossRef

Kaveh, A., & Khayatazad, M. (2012). A new meta-heuristic method: Ray Optimization. Computers & Structures, 112–113, 283–294. https://doi.org/10.1016/j.compstruc.2012.09.003CrossRef

Kaveh, A., & Mahdavi, V. R. (2014). Colliding bodies optimization: A novel meta-heuristic method. Computers & Structures, 139, 18–27. https://doi.org/10.1016/j.compstruc.2014.04.005CrossRef

Kaveh, A., & Talatahari, S. (2010). A novel heuristic optimization method: Charged system search. Acta Mechanica, 213(3), 267–289. https://doi.org/10.1007/s00707-009-0270-4CrossRef

Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of ICNN’95-international conference on neural networks (Vol. 4, pp. 1942–1948). IEEE.

Liang, J., Qu, B., & Suganthan, P. (2013). Problem definitions and evaluation criteria for the CEC 2014 special session and competition on single objective real-parameter numerical optimization. Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore, 635.

Liang, J., Qu, B., Suganthan, P., & Chen, Q. (2014). Problem definitions and evaluation criteria for the CEC 2015 competition on learning-based real-parameter single objective optimization. Technical Report201411A, Computational Intelligence Laboratory, Zhengzhou University, Zhengzhou China and Technical Report, Nanyang Technological University, Singapore.

Lim, W. C. E., Kanagaraj, G., & Ponnambalam, S. G. (2016). A hybrid cuckoo search-genetic algorithm for hole-making sequence optimization. Journal of Intelligent Manufacturing, 27(2), 417–429. https://doi.org/10.1007/s10845-014-0873-zCrossRef

Liu, H., Cai, Z., & Wang, Y. (2010). Hybridizing particle swarm optimization with differential evolution for constrained numerical and engineering optimization. Applied Soft Computing, 10(2), 629–640. https://doi.org/10.1016/j.asoc.2009.08.031CrossRef

Mahdavi, M., Fesanghary, M., & Damangir, E. (2007). An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation, 188(2), 1567–1579. https://doi.org/10.1016/j.amc.2006.11.033CrossRef

Mirjalili, S. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228–249. https://doi.org/10.1016/j.knosys.2015.07.006CrossRef

Mirjalili, S. (2016). SCA: A Sine Cosine algorithm for solving optimization problems. Knowledge-Based Systems, 96, 120–133. https://doi.org/10.1016/j.knosys.2015.12.022CrossRef

Mirjalili, S., Gandomi, A. H., Mirjalili, S. Z., Saremi, S., Faris, H., & Mirjalili, S. M. (2017). Salp swarm algorithm: A bio-inspired optimizer for engineering design problems. Advances in Engineering Software, 114, 163–191. https://doi.org/10.1016/j.advengsoft.2017.07.002CrossRef

Mirjalili, S., & Lewis, A. (2016). The Whale optimization algorithm. Advances in Engineering Software, 95, 51–67. https://doi.org/10.1016/j.advengsoft.2016.01.008CrossRef

Mirjalili, S., Mirjalili, S. M., & Hatamlou, A. (2016). Multi-verse optimizer: A nature-inspired algorithm for global optimization. Neural Computing and Applications, 27(2), 495–513. https://doi.org/10.1007/s00521-015-1870-7CrossRef

Mirjalili, S., Mirjalili, S. M., & Lewis, A. (2014). Grey wolf optimizer. Advances in Engineering Software, 69, 46–61. https://doi.org/10.1016/j.advengsoft.2013.12.007CrossRef

Mishra, A., & Deb, S. (2019). Assembly sequence optimization using a flower pollination algorithm-based approach. Journal of Intelligent Manufacturing, 30(2), 461–482. https://doi.org/10.1007/s10845-016-1261-7CrossRef

Qais, M. H., Hasanien, H. M., & Alghuwainem, S. (2020). Transient search optimization: A new meta-heuristic optimization algorithm. Applied Intelligence, 50(11), 3926–3941. https://doi.org/10.1007/s10489-020-01727-yCrossRef

Rahnamayan, S., Tizhoosh, H. R., & Salama, M. M. A. (2008). Opposition-based differential evolution. IEEE Transactions on Evolutionary Computation, 12(1), 64–79. https://doi.org/10.1109/TEVC.2007.894200CrossRef

Rao, R. (2016). Jaya: A simple and new optimization algorithm for solving constrained and unconstrained optimization problems. International Journal of Industrial Engineering Computations, 7(1), 19–34.

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2011). Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design, 43(3), 303–315. https://doi.org/10.1016/j.cad.2010.12.015CrossRef

Rao, R. V., Savsani, V. J., & Vakharia, D. P. (2012). Teaching–learning-based optimization: An optimization method for continuous non-linear large scale problems. Information Sciences, 183(1), 1–15. https://doi.org/10.1016/j.ins.2011.08.006CrossRef

Rashedi, E., Nezamabadi-pour, H., & Saryazdi, S. (2009). GSA: A gravitational search algorithm. Special Section on High Order Fuzzy Sets, 179(13), 2232–2248. https://doi.org/10.1016/j.ins.2009.03.004CrossRef

Sadollah, A., Bahreininejad, A., Eskandar, H., & Hamdi, M. (2013). Mine blast algorithm: A new population based algorithm for solving constrained engineering optimization problems. Applied Soft Computing, 13(5), 2592–2612. https://doi.org/10.1016/j.asoc.2012.11.026CrossRef

Sadollah, A., Sayyaadi, H., & Yadav, A. (2018). A dynamic metaheuristic optimization model inspired by biological nervous systems: Neural network algorithm. Applied Soft Computing, 71, 747–782. https://doi.org/10.1016/j.asoc.2018.07.039CrossRef

Sandgren, E. (1988). Nonlinear integer and discrete programming in mechanical design. In 14th Design automation conference DETC88 (pp. 95–105). https://doi.org/10.1115/DETC1988-0012

Shi, Y., & Eberhart, R. (1998). A modified particle swarm optimizer. In 1998 IEEE international conference on evolutionary computation proceedings. IEEE world congress on computational intelligence (Cat. No.98TH8360) (pp. 69–73). Presented at the 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence, Anchorage, AK, USA: IEEE. https://doi.org/10.1109/ICEC.1998.699146

Wolpert, D. H., & Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation, 1(1), 67–82. https://doi.org/10.1109/4235.585893CrossRef

Yang, X., & Deb, S. (2009). Cuckoo Search via Lévy flights. In 2009 World congress on nature biologically inspired computing (NaBIC) (pp. 210–214). https://doi.org/10.1109/NABIC.2009.5393690

Yang, X.-S., & Deb, S. (2014). Cuckoo search: Recent advances and applications. Neural Computing and Applications, 24(1), 169–174. https://doi.org/10.1007/s00521-013-1367-1CrossRef

Yuan, S., Li, T., & Wang, B. (2021). A discrete differential evolution algorithm for flow shop group scheduling problem with sequence-dependent setup and transportation times. Journal of Intelligent Manufacturing, 32(2), 427–439. https://doi.org/10.1007/s10845-020-01580-3CrossRef

Zhang, J., Xiao, M., Gao, L., & Pan, Q. (2018). Queuing search algorithm: A novel metaheuristic algorithm for solving engineering optimization problems. Applied Mathematical Modelling, 63, 464–490. https://doi.org/10.1016/j.apm.2018.06.036CrossRef

Zhang, Y., & Jin, Z. (2020). Group teaching optimization algorithm: A novel metaheuristic method for solving global optimization problems. Expert Systems with Applications, 148, 113246. https://doi.org/10.1016/j.eswa.2020.113246CrossRef

Zhou, J., Ye, H., Ji, X., & Deng, W. (2019). An improved backtracking search algorithm for casting heat treatment charge plan problem. Journal of Intelligent Manufacturing, 30(3), 1335–1350. https://doi.org/10.1007/s10845-017-1328-0CrossRef

Titel: Group teaching optimization algorithm with information sharing for numerical optimization and engineering optimization
verfasst von: Yiying Zhang
Aining Chi
Publikationsdatum: 22.11.2021
Verlag: Springer US
Erschienen in: Journal of Intelligent Manufacturing / Ausgabe 4/2023
Print ISSN: 0956-5515
Elektronische ISSN: 1572-8145
DOI: https://doi.org/10.1007/s10845-021-01872-2

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